Association of mixed polycyclic aromatic hydrocarbons exposure with oxidative stress in Korean adults

Polycyclic aromatic hydrocarbons (PAHs) are widespread pollutants associated with several adverse health effects and PAH-induced oxidative stress has been proposed as a potential mechanism. This study evaluated the associations of single and multiple PAHs exposure with oxidative stress within the Korean adult population, using serum gamma glutamyltransferase (GGT) as an oxidative stress marker. Data from the Second Korean National Environmental Health Survey (2012–2014) were analyzed. For analysis, 5225 individuals were included. PAH exposure was assessed with four urinary PAH metabolites: 1-hydroxyphenanthrene, 1-hydroxypyrene, 2-hydroxyfluorene, and 2-naphthol. After adjusting for age, sex, body mass index, drinking, passive smoking, and current smoking (model 1), as well as the presence of diabetes and hepatobiliary diseases (model 2), complex samples general linear model regression analyses for each metabolite revealed a significant positive association between Ln(1-hydroxyphenanthrene) and Ln(GGT) (model 1: β = 0.040, p < 0.01 and model 2: β = 0.044, p < 0.05). For the complete dataset (n = 4378), a significant positive association was observed between mixture of four urinary PAH metabolites and serum GGT in both the quantile g-computation and the Bayesian kernel machine regression analysis. Our study provides evidence for the association between mixed PAH exposure and oxidative stress.


Study participants
This study analyzed the data derived from the Second Korean National Environmental Health Survey (KoNEHS), conducted by the National Institute of Environmental Research (NIER) between 2012 and 2014.KoNEHS, which has a cross-sectional nature, is a nationwide biomonitoring survey designed to identify the extent and source of exposure to environmental pollutants in Korean population 26 .The survey applied stratified two-stage sampling and included 6,478 subjects aged 19 and over from 400 districts, reflecting the population distribution.Data were gathered via personal interviews and biological sampling.A subset of the KoNEHS population was selected for analysis (n = 5225), comprising individuals with serum GGT levels lower than 100 U/L without missing values, and with no excessive alcohol use.Because high GGT levels may indicate a disease state regardless of oxidative stress, individuals with GGT levels above 100 U/L were excluded from the analysis.Additionally, individuals with excessive alcohol use were excluded because it can affect GGT independently of oxidative stress.Excessive alcohol use included binge drinking (n = 165) (about ≥ 4 drinks per occasion for women; ≥ 5 for men) and heavy drinking (n = 921) (about ≥ 8 drinks per week for women; ≥ 15 for men).A single drink was defined as approximately 14 g of alcohol per serving 27 .

Serum GGT, urine PAH metabolites, and covariates
Serum GGT levels was employed as an oxidative stress marker.PAH exposure was assessed through urinary PAH metabolites: 1-hydroxyphenanthrene, 1-hydroxypyrene, 2-hydroxyfluorene, and 2-naphthol.Naphthol, hydroxyfluorene, hydroxyphenanthrene, and hydroxypyrene, which are urinary metabolites of low-to mediummolecular-weight PAHs including naphthalene, fluorene, phenanthrene and pyrene, constitute the majority of urinary PAH metabolites, and these urinary hydroxy-PAHs are commonly used as exposure biomarkers for PAHs 28,29 .Detailed information on the method of analysis has been described elsewhere 26,[30][31][32] .In brief, levels of serum GGT and urinary PAH metabolites were based on individual blood and spot urine samples, respectively.Both samples were taken at the same time.Serum GGT levels were evaluated using colorimetry, and concentrations of urinary PAH metabolites were assessed with gas chromatography-mass spectrometry (GC-MS).The analytical range for serum GGT was 4.0-1,200 U/L.The limits of detection (LODs) for each metabolite were 0.047 µg/L for 1-hydroxyphenanthrene, 0.015 µg/L for 1-hydroxypyrene, 0.04 µg/L for 2-hydroxyfluorene, and 0.05 µg/L for 2-naphthol.Concentrations below the LOD for each metabolite were substituted by a value which is each metabolite's LOD divided by the square root of 2. Urinary PAH metabolite concentrations were adjusted by urine creatinine concentrations in the range of (0.3-3.0 g/L).Each urinary PAH metabolite was sorted into four groups by quartiles.
Variables included age, sex, body mass index (BMI), current drinking status, current smoking status, and the presence of passive smoking.BMI was divided into < 18 kg/m 2 vs. 18 ≤ and < 25 kg/m 2 vs. 25 ≤ kg/m 2 .Drinking status was divided as currently drinking vs. currently not drinking.Smoking status was grouped as currently smoking vs. currently not smoking (including ex-and non-smokers).Additionally, the presence or absence of diabetes or hepatobiliary diseases was also included as variables since these can affect the level of serum GGT 22,33 .The response rate for questionnaire items related to the current history of diseases such as diabetes and hepatobiliary disease was 46.2% (n = 2999).

Statistical analyses
Stratum, cluster, and weight were incorporated into analyses because the KoNEHS has the stratified two-stage cluster sampling structure.Given that the distributions of serum GGT and urinary PAH metabolite concentrations were skewed, they were subjected to logarithmic transformation.The estimated geometric means (GMs) of serum GGT levels were compared across quartile groups of four urinary metabolites of PAHs in the total KoNEHS population (n = 6478) and subpopulation (n = 5225).The relationships between single urinary PAH metabolites and serum GGT levels were assessed for the subpopulation using complex samples general linear model (CSGLM) regression analyses.Age, sex, BMI, drinking, and smoking status were adjusted (model 1), and the presence of diabetes or hepatobiliary diseases was also taken into account (model 2).Complex samples statistical analyses were performed using SPSS v25 for Windows (IBM, Armonk, NY, USA).
Quantile g-computation (qg-computation) and Bayesian kernel machine regression (BKMR) were used to assess the mixed effect of PAH exposures on serum GGT.Qg-computation evaluates the effect of a simultaneous increase in one quantile of every exposure on the outcome by estimating the parameters of a marginal structural model 34 .Also, qg-computation evaluates for both positive and negative effects of exposures on the outcome 34 .BKMR is a nonparametric method that allows the application of kernel function to assess the joint effects of exposures, considering nonlinear relationships and/or potential interactions 35 .For the complete dataset with no missing values for four urinary PAH metabolites (n = 4378), qg-computation and BKMR were conducted using the bkmr v0.2.2/ bkmrhat v1.1.3packages and the qgcomp v2.10.1 package in R v4.3.1 (R Development Core Team, Vienna, Austria).Both models were adjusted for age, sex, BMI, current smoking, and current drinking.In the qg-computation model, 10-quantiles were applied for the exposure variables.The BKMR model was fitted using Markov chain Monte Carlo (MCMC) with 80,000 iterations which included 40,000 burn-in iterations.In the BKMR analysis, the default setting of the bkmr and bkmrhat packages was used, and posterior inclusion probabilities (PIPs) were computed to show the importance of each PAH metabolite in the mixture.Statistical significance was evaluated at p < 0.05.

Ethics approval and consent to participate
This study using the 2nd KoNEHS data received approval from the Institutional Review Board of Inje University Haeundae Paik Hospital (No. 2022-11-013).The KoNEHS was approved by the Institutional Review Board of NIER and informed consent was obtained from all participants.All methods used in this study were performed in accordance with relevant guidelines and regulations.

Results
Table 1 outlines the general characteristics of subpopulation (n = 5225) and total the KoNEHS population (n = 6478).The estimated percentage of subjects with values exceeding 100 U/L or missing values for serum GGT was 5.1% (n = 332) among total population.Subjects with excessive alcohol use constituted an estimated 20.5% of the total population (n = 1086).The distributions of each PAH metabolite for the entire population of the KoNEHS are shown in Supplementary Table S1.
Table 2 displays the estimated GMs of serum GGT by quartile groups of urinary PAH metabolites for the total population and subpopulation.The GMs of the fourth quartiles of each PAH metabolite significantly differed from those of the first quartiles in both the total population and subpopulation (p < 0.01).The estimated GMs of serum GGT appeared to increase significantly with the progression of quartiles in all PAH metabolites (p for trend < 0.01).
The associations of serum GGT levels with the concentration of each urinary PAH metabolite in the subpopulation, as assessed using CSGLM regression, are shown in Table 3. 1-Hydroxyphenanthrene was significantly positively associated with serum GGT in both model 1 (β = 0.040, p < 0.01) and model 2 (β = 0.044, p < 0.05).2-Hydroxyfluorene showed a significant association with serum GGT only in model 1 (β = 0.034, p < 0.05).We found no significant associations with serum GGT among the other metabolites.
Table 5 presents the posterior inclusion probabilities (PIPs) for each PAH metabolite in the BKMR model.Among the metabolites, 1-hydroxyphenanthrene was most strongly associated with serum GGT (PIP = 0.8402).Figure 2 shows associations between PAH metabolites and serum GGT using the BKMR model.Figure 2A illustrates the univariate associations between each PAH metabolite and serum GGT when fixing others to the median, showing that urinary 1-hydroxyphenanthrene has a significant positive association. Figure 2B shows www.nature.com/scientificreports/ the overall effect of the mixture of PAH metabolites on serum GGT and there was a strong positive association between the mixture of PAH metabolites and serum GGT.In the evaluation of individual effects (Fig. 2C), higher level of 1-hydroxyphenanthrene was significantly associated with higher serum GGT outcome.

Discussion
This study examined the relationship of single and mixed PAH exposure with serum GGT.Among the individual PAHs, urinary 1-hydroxyphenanthrene showed a significant positive association with serum GGT.For combined PAH exposure, the mixture of four urinary PAH metabolites was positively associated with serum GGT levels in both quantile g-computation and BKMR analysis.Within the normal range, serum GGT has been suggested as an oxidative stress biomarker due to the intracellular antioxidant activity of GGT 24 .Our findings imply that PAH exposure is positively correlated with oxidative stress.Several studies have evaluated the association between PAH exposure and oxidative stress.For example, there was a significant positive correlation between urinary 1-hydroxypyrene and 8-OHdG in coke-oven workers 36 , and urinary 1-hydroxypyrene was significantly associated with 8-OHdG in kitchen staff and asphalt workers 37,38 .Urinary 1-hydroxypyrene was associated with urinary MDA in schoolchildren 17 , and a dose-response relationship was observed between urinary PAH metabolites and 8-OHdG in the general population of China 18 .A German population study found significantly positive relationships of urinary hydroxy-PAHs with biomarkers of oxidative stress, including MDA, 8-OHdG, and F2α-isoprostanes 39 .Similarly, in the Chinese population, urinary hydroxy-PAHs had positive associations with urinary MDA 40,41 .Also, in terms of oxidative stress, the relationships between PAH metabolites and serum GGT have been evaluated in several studies, with recent www.nature.com/scientificreports/studies indicating a significant association between urinary PAH metabolites and serum GGT in the US adult 42 and adolescent 43 populations.
Our study revealed that the mixture of four urinary PAH metabolites had significant positive associations with serum GGT levels after adjusting for covariates, with 1-hydroxyphenanthrene having the most substantial impact.These results align with those of several prior studies.The increase in urinary 1-hydroxyphenanthrene was shown to be the most substantial estimated percentage change in urinary 8-OHdG among various PAH www.nature.com/scientificreports/metabolites 18 .Another study indicated that a 100% increase in the sum of urinary hydroxyphenanthrenes was associated with a 22.4% increase in MDA, which represented the largest percent change compared to other PAH metabolites among healthy subjects 41 .Among PAH metabolites, 1-hydroxyphenanthrene was found to be the most strongly associated with serum GGT levels, followed by 2-and 3-hydroxyphenanthrene 42 .However, associations with oxidative stress have yielded different results depending on the PAH metabolites evaluated, and few studies have considered combined exposure to multiple PAHs.A pilot study of the Chinese population evaluated the joint effect of hydroxy-PAHs on 8-OHdG, as well as oxygen radical antioxidant capacity (ORAC) and hydroxyl radical antioxidant capacity (HORAC), which are employed as indicators of antioxidant capacity, using BKMR 44 .The BKMR models indicated positive relationships between the eight hydroxy-PAHs and urinary 8-OHdG, as well as plasma ORAC and HORAC activity.Moreover, urinary 2-plus 3-hydroxyphenanthrene contributed significantly to the increase in urinary 8-OHdG levels among a mixture of urinary  PAH metabolites 44 .Another study evaluated the association between mixed PAH exposure and serum GGT in relation to liver function using weighted quantile sum regression in the US adolescent population 45 .However, unlike our study, the study did not detect a association between serum GGT and either individual or mixed urinary PAH metabolites.This discrepancy could be attributed to variations in the age groups of the study populations or the levels or durations of PAH exposure between studies.Antioxidant defenses may also vary among age groups 46,47 .www.nature.com/scientificreports/Several mechanisms have been proposed for the association of PAH exposure with oxidative stress.PAHs are metabolized through cytochrome P450 enzymes (CYPs) such CYP1A1/2 and CYP1B1, and then become reactive intermediates that can covalently bind with DNA, causing carcinogenicity, and can establish redox cycles, leading to ROS generation 16 .PAH exposure may also be involved in regulating ROS-generating enzymes such as CYPs, through the aryl hydrocarbon receptor signaling pathway, leading to oxidative stress 48 .
Beyond the carcinogenic properties of PAHs, numerous studies have demonstrated associations of PAH exposure with other diseases, including CVDs 49 , allergic diseases 50 , diabetes 51 , and neurodegenerative diseases 13 .Although the underlying mechanisms are not well understood, oxidative stress induced by PAH exposure could be an underlying mechanism for these associations.Oxidative stress can cause chronic inflammation in the human body, which affects the progression of various diseases 52 .Oxidative stress related-inflammation has been proposed as a mechanism for endothelial dysfunction and arterial damage, which lead to CVDs 53 .Serum GGT has also been proposed as a potential marker for CVDs 22,54 , and this is thought to be related to its role as a marker of oxidative stress.Chronic oxidative stress can affect the regulation of neuroinflammatory response and cause neuroinflammation, leading to neurodegenerative diseases such as Parkinson's and Alzheimer's diseases 55 .ROS are also linked to the induction of allergic inflammation 56 .This is the first study to evaluate the relationship between PAH exposure and oxidative stress from the perspective of mixed PAHs in Korean adults.We employed two independent analytical methods to evaluate the mixture effect, and found a positive association between mixed PAH exposure and oxidative stress.Previously, there have been few studies assessing association between mixed PAH exposure and oxidative stress.Nevertheless, our study has some limitations.Firstly, it is limited in evaluating causal relationships due to the cross-sectional nature of KoNEHS.Secondly, only serum GGT was included as an oxidative stress marker in this study because other oxidative stress markers such as MDA and 8-OHdG were not investigated in the second KoNEHS.Thirdly, due to the short half-lives of PAH metabolites, urinary levels of PAH metabolites in spot urine may not fully reflect the actual history of PAH exposure.However, since this is a study using national survey data, it can be helpful in understanding overall trends in the population.

Conclusion
This study revealed a significant association between mixed PAH exposure and oxidative stress, providing an evidence for the overall effect of complex PAH exposure on oxidative stress response.Further studies are required to clarify this association.

Figure 2 .
Figure 2. Associations of PAH metabolites with serum GGT from Bayesian kernel machine regression in the complete dataset (n = 4378).(A) Univariate exposure-response function h(exposure) of each PAH metabolite, where all other PAH metabolites were fixed at their 50th percentiles.(B) Joint effect of mixed PAH metabolites on serum GGT, calculated by comparing the outcome when all PAH metabolites were at a specific percentile from the 25th to 75th percentile with when all metabolites were at the 50th percentile.C. Individual effects of each PAH metabolite on serum GGT outcome, representing changes of outcome in each PAH metabolite from its 75th to its 25th percentile, where other PAH metabolites were fixed at the 25th, 50th, and 75th percentiles.The dots represent the estimate, and the lines depict the 95% credible intervals.All PAH metabolites were log-transformed.The model was adjusted for age, sex, BMI, current smoking, and current drinking.1-OHP 1-hydroxypyrene, 2-NAP 2-naphthol, 1-PHE 1-hydroxyphenanthrene, 2-FLU 2-hydroxyfluorene, PAH polycyclic aromatic hydrocarbon, GGT gamma glutamyltransferase, BMI body mass index.

Table 1 .
General characteristics of study population.a Subpopulation excluding subjects with serum GGT above 100 U/L or excessive alcohol use.b The response rate for the questionnaire items related to current history of diseases was 46.2% (n = 2999).c This category includes subjects who have missing values for serum GGT (n = 22).

Table 2 .
Estimated geometric mean of serum GGT by quartile groups of urinary PAH metabolites.GGT gamma glutamyltransferase, PAH polycyclic aromatic hydrocarbon, KoNEHS Korean National Environmental Health Survey, GM geometric mean, CI confidence interval.a Subpopulation excluding subjects with serum GGT above 100 U/L or excessive alcohol use.b Estimated geometric mean and 95% confidence interval from complex samples general linear model regression.*Significantly different compared to first quartile (p < 0.01).

Table 3 .
Association between serum GGT levels and urinary PAH metabolites in subpopulation a (n = 5225).GGT gamma glutamyltransferase, PAH polycyclic aromatic hydrocarbon, CSGLM complex samples general linear model, SE standard error, BMI body mass index.

Table 5 .
Posterior inclusion probability for each PAH metabolite on serum GGT in the complete dataset (n = 4378).PIP posterior inclusion probability, PAH polycyclic aromatic hydrocarbon, GGT gamma glutamyltransferase.